Audio feature optimization based on the PSO and attribute importance

This paper presents a novel approach to achieve optimization for the audio features in compressed domain, which is the PSO (particle swarm optimization) algorithm basing on the attribute importance criterion of rough set theory. Our method firstly extracts the attributes of audio to form the feature vectors and pre-processes these vectors, then realizes the optimization using the proposed PSO algorithm, and finally determines the optimal feature subset. The experimental results show that feature optimization not only greatly reduces the training time of classifier, but also improves the classification accuracy. The performance of the classification model developed on the optimal feature subset. It achieves effective dimensionality reduction.